SoroushVahidi/scidocs huggingface dataset:
RIC: Relaxed Inclusion Caches for mitigating LLC side-channel attacks
(2017)

Recently, side-channel attacks on Last Level Caches (LLCs) were demonstrated. The attacks require the ability to evict critical data from the cache hierarchy, making future accesses visible. We propose Relaxed Inclusion Caches (RIC), a low-complexity cache design protecting against LLC side channel attacks. RIC relaxes inclusion when it is not needed, preventing the attacker from replacing the victim's data from the local core caches thus protecting critical data from leakage. RIC improves performance (by about 10%) and retains snoop filtering capabilities of inclusive cache hierarchies, while requiring only minimal changes to the cache.
Curcumin: From ancient medicine to current clinical trials
(2008)

Curcumin is the active ingredient in the traditional herbal remedy and dietary spice turmeric (Curcuma longa). Curcumin has a surprisingly wide range of beneficial properties, including anti-inflammatory, antioxidant, chemopreventive and chemotherapeutic activity. The pleiotropic activities of curcumin derive from its complex chemistry as well as its ability to influence multiple signaling pathways, including survival pathways such as those regulated by NF-κB, Akt, and growth factors; cytoprotective pathways dependent on Nrf2; and metastatic and angiogenic pathways. Curcumin is a free radical scavenger and hydrogen donor, and exhibits both pro- and antioxidant activity. It also binds metals, particularly iron and copper, and can function as an iron chelator. Curcumin is remarkably non-toxic and exhibits limited bioavailability. Curcumin exhibits great promise as a therapeutic agent, and is currently in human clinical trials for a variety of conditions, including multiple myeloma, pancreatic cancer, myelodysplastic syndromes, colon cancer, psoriasis and Alzheimer’s disease.
Interference-Aware Channel Assignment in Multi-Radio Wireless Mesh Networks
(2006)

The capacity problem in wireless mesh networks can be alleviated by equipping the mesh routers with multiple radios tuned to non-overlapping channels. However, channel assignment presents a challenge because co-located wireless networks are likely to be tuned to the same channels. The resulting increase in interference can adversely affect performance. This paper presents an interference-aware channel assignment algorithm and protocol for multi-radio wireless mesh networks that address this interference problem. The proposed solution intelligently assigns channels to radios to minimize interference within the mesh network and between the mesh network and co-located wireless networks. It utilizes a novel interference estimation technique implemented at each mesh router. An extension to the conflict graph model, the multi-radio conflict graph, is used to model the interference between the routers. We demonstrate our solution’s practicality through the evaluation of a prototype implementation in a IEEE 802.11 testbed. We also report on an extensive evaluation via simulations. In a sample multi-radio scenario, our solution yields performance gains in excess of 40% compared to a static assignment of channels.
An Edge Based Smart Parking Solution Using Camera Networks and Deep Learning
(2018)

The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. For urban dwellers, few things are more irritating than anxiously searching for a parking space. Research results show that as much as 30% of traffic is caused by drivers driving around looking for parking spaces in congested city areas. There has been considerable activity among researchers to develop smart technologies that can help drivers find a parking spot with greater ease, not only reducing traffic congestion but also the subsequent air pollution. Many existing solutions deploy sensors in every parking spot to address the automatic parking spot detection problems. However, the device and deployment costs are very high, especially for some large and old parking structures. A wide variety of other technological innovations are beginning to enable more adaptable systems—including license plate number detection, smart parking meter, and vision-based parking spot detection. In this paper, we propose to design a more adaptable and affordable smart parking system via distributed cameras, edge computing, data analytics, and advanced deep learning algorithms. Specifically, we deploy cameras with zoom-lens and motorized head to capture license plate numbers by tracking the vehicles when they enter or leave the parking lot; cameras with wide angle fish-eye lens will monitor the large parking lot via our custom designed deep neural network. We further optimize the algorithm and enable the real-time deep learning inference in an edge device. Through the intelligent algorithm, we can significantly reduce the cost of existing systems, while achieving a more adaptable solution. For example, our system can automatically detect when a car enters the parking space, the location of the parking spot, and precisely charge the parking fee and associate this with the license plate number.
Role of Emotional Intelligence for Academic Achievement for Students
(2013)

In the current competitive environment where students are expected to perform multi roles with efficiency and effectiveness, it is highly needed to develop their right attitude and emotional intelligence towards the unseen complexities of life and quality education. As emotional intelligence is a subset of social intelligence with the ability to understand and monitor one’s own feelings and others too which allows a student to mine the required data for his academic achievement which is an outcome of education and the extent at which the educational goal has been achieved. The emphasis of this paper was to determine the factors which are affecting the development of emotional intelligence and its role in academic achievement for students. In this research secondary data has been collected out of which we find out the correlation between emotional intelligence and academic achievement and teaching emotional and social skills at school not only positively influence academic achievement during the year when these were taught but also leaves the impact in long term achievement. Findings of this paper present that academic achievement without emotional intelligence does not indicate future success and absence of emotional intelligence also indicate the week personality and ability to build relations at working place as well in schools and it is highly important for quality education.
Adaptive Clock Gating Technique for Low Power IP Core in SoC Design
(2007)

Clock gating is a well-known technique to reduce chip dynamic power. This paper analyzes the disadvantages of some recent clock gating techniques and points out that they are difficult in system-on-chip (SoC) design. Based on the analysis of the intellectual property (IP) core model, an adaptive clock gating (ACG) technique which can be easily realized is introduced for the low power IP core design. ACG can automatically enable or disable the IP clock to reduce not only dynamic power but also leakage power with power gating technique. The experimental results on some IP cores in a real SoC show an average of 62.2% dynamic power reduction and 70.9% leakage power reduction without virtually performance impact.
Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing
(2010)

Digital game-based learning (DGBL) has become a viable instructional option in recent years due to its support of learning motivation. Recent studies have mostly focused on identifying motivational factors in digital games (e.g., curiosity, rules, control) that support intrinsic motivation. These findings, however, are limited in two fronts. First, they did not depict the interactive nature of the motivational processing in DGBL. Second, they excluded the outcome processing (learners’ final effort versus performance evaluation) as a possible motivation component to sustain the iterative game playing cycle. To address these problems, situated in the integrative theory of Motivation, Volition, and Performance (MVP), this study examined the relationship between motivational processing and outcome processing in an online instructional game. The study surveyed 264 undergraduate students after playing the Trade Ruler online game. Based on the data collected by ARCS-based Instructional Materials Motivational Survey (IMMS), a regression analysis revealed a significant model between motivational processing (attention, relevance, and confidence) and the outcome processing (satisfaction). The finding preliminarily suggests that both motivational processing and outcome processing need to be considered when designing DGBL. Furthermore, the finding implies a potential relationship between intrinsic motives and extrinsic rewards in DGBL. Published by Elsevier Ltd.
Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder
(2017)

Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model that lets the attention depend on the source-side syntax. Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word coverage.1
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
(2015)

Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. TreeLSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
(2018)

Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntaxaware versions on the English–German language pair.
Improved Neural Machine Translation with Source Syntax
(2017)

Neural Machine Translation (NMT) based on the encoder-decoder architecture has recently achieved the state-of-the-art performance. Researchers have proven that extending word level attention to phrase level attention by incorporating source-side phrase structure can enhance the attention model and achieve promising improvement. However, word dependencies that can be crucial to correctly understand a source sentence are not always in a consecutive fashion (i.e. phrase structure), sometimes they can be in long distance. Phrase structures are not the best way to explicitly model long distance dependencies. In this paper we propose a simple but effective method to incorporate source-side long distance dependencies into NMT. Our method based on dependency trees enriches each source state with global dependency structures, which can better capture the inherent syntactic structure of source sentences. Experiments on Chinese-English and English-Japanese translation tasks show that our proposed method outperforms state-of-the-art SMT and NMT baselines.
Performance evaluation of local colour invariants
(2009)

In this paper, we compare local colour descriptors to grey-value descriptors. We adopt the evaluation framework of Mikolayzcyk and Schmid. We modify the framework in several ways. We decompose the evaluation framework to the level of local grey-value invariants on which common region descriptors are based. We compare the discriminative power and invariance of grey-value invariants to that of colour invariants. In addition, we evaluate the invariance of colour descriptors to photometric events such as shadow and highlights. We measure the performance over an extended range of common recording conditions including significant photometric variation. We demonstrate the intensity-normalized colour invariants and the shadow invariants to be highly distinctive, while the shadow invariants are more robust to both changes of the illumination colour, and to changes of the shading and shadows. Overall, the shadow invariants perform best: they are most robust to various imaging conditions while maintaining discriminative power. When plugged into the SIFT descriptor, they show to outperform other methods that have combined colour information and SIFT. The usefulness of C-colour-SIFT for realistic computer vision applications is illustrated for the classification of object categories from the VOC challenge, for which a significant improvement is reported. 2008 Elsevier Inc. All rights reserved.
Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction
(2013)

Web search result clustering aims to facilitate information search on the Web. Rather than the results of a query being presented as a flat list, they are grouped on the basis of their similarity and subsequently shown to the user as a list of clusters. Each cluster is intended to represent a different meaning of the input query, thus taking into account the lexical ambiguity (i.e., polysemy) issue. Existing Web clustering methods typically rely on some shallow notion of textual similarity between search result snippets, however. As a result, text snippets with no word in common tend to be clustered separately even if they share the same meaning, whereas snippets with words in common may be grouped together even if they refer to different meanings of the input query.In this article we present a novel approach to Web search result clustering based on the automatic discovery of word senses from raw text, a task referred to as Word Sense Induction. Key to our approach is to first acquire the various senses (i.e., meanings) of an ambiguous query and then cluster the search results based on their semantic similarity to the word senses induced. Our experiments, conducted on data sets of ambiguous queries, show that our approach outperforms both Web clustering and search engines.
Towards multi-cue urban curb recognition
(2013)

This paper presents a multi-cue approach to curb recognition in urban traffic. We propose a novel texture-based curb classifier using local receptive field (LRF) features in conjunction with a multi-layer neural network. This classification module operates on both intensity images and on three-dimensional height profile data derived from stereo vision. We integrate the proposed multi-cue curb classifier as an additional measurement module into a state-of-the-art Kaiman filter-based urban lane recognition system. Our experiments involve a challenging real-world dataset captured in urban traffic with manually labeled ground-truth. We quantify the benefit of the proposed multi-cue curb classifier in terms of the improvement in curb localization accuracy of the integrated system. Our results indicate a 25% reduction of the average curb localization error at real-time processing speeds.
An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market
(2016)

This paper evaluates the performance of a number of modelling approaches for future mortgage default status. Boosted regression trees, random forests, penalised linear and semi-parametric logistic regression models are applied to four portfolios of over 300,000 Irish owner-occupier mortgages. The main findings are that the selected approaches have varying degrees of predictive power and that boosted regression trees significantly outperform logistic regression. This suggests that boosted regression trees can be a useful addition to the current toolkit for mortgage credit risk assessment by banks and regulators.
Can Personality Be Changed ? The Role of Beliefs in Personality and Change
(2008)

Using recent research, I argue that beliefs lie at the heart of personality and adaptive functioning and that they give us unique insight into how personality and functioning can be changed. I focus on two classes of beliefs—beliefs about the malleability of self-attributes and expectations of social acceptance versus rejection—and show how modest interventions have brought about important real-world changes. I conclude by suggesting that beliefs are central to the way in which people package their experiences and carry them forward, and that beliefs should play a more central role in the study of personality. KEYWORDS—personality; personality theories; change; self-beliefs; interventions
Business Intelligence and Business Process Management in Banking Operations
(2007)

Success of banking operations is strongly correlated with the quality of customer relations and efficacy of banks processes. Banks seek means for efficient analysis of vast amount of gathered data from their IT systems. They are exploiting business intelligence (BI) technology to analyze every aspect of their data to understand behavior of their clients, striving to satisfy client's needs, in an endless race for a competitive advantage in the market. The intention of this paper is to review and discuss the most significant applications of business intelligence in banking as well as point to new technology trends that will affect bank's development.
Pitch Extraction and Fundamental Frequency: History and Current Techniques
(2003)

Pitch extraction (also called fundamental frequency estimation) has been a popular topic in many fields of research since the age of computers. Yet in the course of some 50 years of study, current techniques are still not to a desired level of accuracy and robustness. When presented with a single clean pitched signal, most techniques do well, but when the signal is noisy, or when there are multiple pitch streams, many current pitch algorithms still fail to perform well. This report presents a discussion of the history of pitch detection techniques, as well as a survey of the current state of the art in pitch detection technology.
High Performance Methods for Linked Open Data Connectivity Analytics
(2018)

The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or “slices” (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.
Information Technology, Materiality, and Organizational Change: A Professional Odyssey
(2013)

We begin with a retrospective reflection on the first author’s research career, which in large part is devoted to research about the implications of information technology (IT) for organizational change. Although IT has long been associated with organizational change, our historical review of the treatment of technology in organization theory demonstrates how easily the material aspects of organizations can disappear into the backwaters of theory development. This is an unfortunate result since the material characteristics of IT initiatives distinguish them from other organizational change initiatives. Our aim is to restore materiality to studies of IT impact by tracing the reasons for its disappearance and by offering options in which IT’s materiality plays a more central theoretical role. We adopt a socio-technical perspective that differs from a strict sociomaterial perspective insofar as we wish to preserve the ontological distinction between material artifacts and their social context of use. Our analysis proceeds using the concept of “affordance” as a relational concept consistent with the socio-technical perspective. We then propose extensions of organizational routines theory that incorporate material artifacts in the generative system known as routines. These contributions exemplify two of the many challenges inherent in adopting materiality as a new research focus in the study of IT’s organizational impacts.
Electronic travel aids for the blind based on sensory substitution
(2010)

With the development of advanced human-machine interface, effective information processing algorithms, and more powerful microprocessors, it is possible to enable the blind to achieve additional perception of the environment. In 1970s, the blind-assistant facility based on sensory substitution was introduced[1]. It called as ETA (short for Electronic Travel Aids), bases on the other natural senses of the blind, such as hearing, touch, smell, feeling and etc. Then the research of vision aids has been broadly extended. After introducing the traditional methods for guiding blind, two typical input modes of travel aid are presented in this paper. One is sonar-based ETAs and the other technique is camera-based ETAs. Its principle of each modes is discussed in detail. Besides these, comparisons between the above two techniques have also been indicated in this paper.
0-knowledge fuzzing
(2010)

Nowadays fuzzing is a pretty common technique used both by attackers and software developers. Currently known techniques usually involve knowing the protocol/format that needs to be fuzzed and having a basic understanding of how the user input is processed inside the binary. In the past since fuzzing was little-used obtaining good results with a small amount of effort was possible. Today finding bugs requires digging a lot inside the code and the user-input as common vulnerabilies are already identified and fixed by developers. This paper will present an idea on how to effectively fuzz with no knowledge of the userinput and the binary. Specifically the paper will demonstrate how techniques like code coverage, data tainting and inmemory fuzzing allow to build a smart fuzzer with no need to instrument it.